Medical Image Retrieval Using Multi-Texton Assignment.
In this paper, we present a multi-texton representation method for medical image retrieval, which utilizes the locality constraint to encode each filter bank response within its local-coordinate system consisting of the <italic>k</italic> nearest neighbors in texton dictionary and subsequently emplo...
| Publicado en: | Journal of Digital Imaging Vol. 31; no. 1; pp. 107 - 117 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=127707189&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 127707189 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2018 vid: 31 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 127707189 127707189 144013013 127707189 10.1007/s10278-017-0017-z 127707189 ppf: 107 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Medical Image Retrieval Using Multi-Texton Assignment. aug: au: Tang, Qiling Yang, Jirong Xia, Xianfu affil: South Central University for Nationalities, College of Biomedical Engineering, 430074, Wuhan, People’s Republic of China sug: subj: Diagnostic Imaging Information Retrieval Methods Image Processing, Computer Assisted Algorithms Human Experimental Studies Mammography Funding Source ab: In this paper, we present a multi-texton representation method for medical image retrieval, which utilizes the locality constraint to encode each filter bank response within its local-coordinate system consisting of the <italic>k</italic> nearest neighbors in texton dictionary and subsequently employs spatial pyramid matching technique to implement feature vector representation. Comparison with the traditional nearest neighbor assignment followed by texton histogram statistics method, our strategies reduce the quantization errors in mapping process and add information about the spatial layout of texton distributions and, thus, increase the descriptive power of the image representation. We investigate the effects of different parameters on system performance in order to choose the appropriate ones for our datasets and carry out experiments on the IRMA-2009 medical collection and the mammographic patch dataset. The extensive experimental results demonstrate that the proposed method has superior performance. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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